{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset returns four NumPy arrays\n",
    "# The images are 28x28 NumPy arrays\n",
    "# pixel values ranging between 0 and 255.\n",
    "# The labels are an array of integers, ranging from 0 to 9, respond to class_name. \n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_images, train_labels),(test_images, test_labels) = fashion_mnist.load_data()\n",
    "\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7ebcae6c88>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f7ebc987b00>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0], cmap = plt.cm.binary)\n",
    "plt.colorbar()\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocess the data(Normalizaton)\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ebc9f90b8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text xticklabel objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text yticklabel objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap = plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setup the layers\n",
    "model = keras.Sequential([\n",
    "   \n",
    "    keras.layers.Flatten(),\n",
    "    #The first Dense layer has 128 nodes (or neurons). \n",
    "    keras.layers.Dense(units = 128, activation = 'relu'),\n",
    "    #The second (and last) layer is a 10-node softmax layer\n",
    "    #this returns an array of 10 probability scores that sum to 1. \n",
    "    keras.layers.Dense(units = 10, activation = 'softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels\n",
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compile the model\n",
    "#Before the model is ready for training, it needs a few more settings. \n",
    "model.compile(\n",
    "    optimizer = keras.optimizers.SGD(lr=1e-4, decay=1e-6),\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add some callbacks\n",
    "cbEarlyStopping = keras.callbacks.EarlyStopping(monitor='loss', min_delta=0.01, patience=5)\n",
    "cbTensorBoard = keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True, write_grads=True, write_images=True)\n",
    "#kernal command : tensorboard --logdir=/full_path_to_your_logs\n",
    "callbacks = [cbEarlyStopping, cbTensorBoard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/5\n",
      "48000/48000 [==============================] - 6s 122us/step - loss: 2.1826 - acc: 0.2163 - val_loss: 2.0304 - val_acc: 0.3443\n",
      "Epoch 2/5\n",
      "48000/48000 [==============================] - 6s 118us/step - loss: 1.9211 - acc: 0.4193 - val_loss: 1.8177 - val_acc: 0.4727\n",
      "Epoch 3/5\n",
      "48000/48000 [==============================] - 6s 121us/step - loss: 1.7365 - acc: 0.5207 - val_loss: 1.6508 - val_acc: 0.5643\n",
      "Epoch 4/5\n",
      "48000/48000 [==============================] - 6s 118us/step - loss: 1.5872 - acc: 0.5905 - val_loss: 1.5143 - val_acc: 0.6204\n",
      "Epoch 5/5\n",
      "48000/48000 [==============================] - 6s 117us/step - loss: 1.4641 - acc: 0.6322 - val_loss: 1.4015 - val_acc: 0.6481\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7ebc4847b8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model\n",
    "model.fit(train_images, train_labels, epochs = 5, validation_split = 0.2, callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 46us/step\n",
      "Test Accuracy: 0.6386\n"
     ]
    }
   ],
   "source": [
    "#Evaluate accuracy\n",
    "test_loss, test_accuracy = model.evaluate(test_images, test_labels)\n",
    "print('Test Accuracy:', test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions\n",
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualization code\n",
    "def plot_image(i, predictions_array, true_labels, img):\n",
    "    prediction_array, true_label, img = predictions_array[i], true_labels[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    \n",
    "    plt.imshow(img, cmap = plt.cm.binary)\n",
    "    \n",
    "    predicted_label = np.argmax(prediction_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    plt.xlabel(\n",
    "        \"{} {:2.0f}% ({})\".format(\n",
    "        class_names[predicted_label],\n",
    "        100*np.max(prediction_array),\n",
    "        class_names[true_label]\n",
    "    )\n",
    "        , color = color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_labels):\n",
    "    prediction_array, true_label = predictions_array[i], true_labels[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10), class_names, rotation = 90)\n",
    "    plt.yticks([])\n",
    "    \n",
    "    thisplot = plt.bar(range(10), prediction_array, color = \"blue\")\n",
    "    plt.ylim([0,1])\n",
    "    predicted_label = np.argmax(prediction_array)\n",
    "    \n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x216 with 0 Axes>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ebcd00f28>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7eeeaeee10>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wZeC2SEbHM8CoSg/djC3M+GDO6xxnRh8zdiMMNT0D/AE4OWs7Dti5TnnsMx1H+KWxSXP3pv5Rql061+yJ91hPVwTkk4BbO5TdQgiOZZwB9Mt6meu5M43Qm3zYjHnAzcQD5irgg2bMIvTUf5CVnwJcYsZcQo+xXvl1hLHS2dn4bbWfZteenj1/Zcn3dgFhWADCOPAXgEeAH0fq3smG4ZO/Bn7XYcjhN8BRZmxV76LuzCCMK/+2OrXQnXcIv7wuMmMOYQjpYzkv8wzhl9WdwFfcWUP4xdmS/X3cBHwhu8e88vuA3bPP7ITsdQ8iZFuISAdZxoOkwIxbgbOycdvNjhnbEiZwD+7pe2mWmQ0G3N1X9PS9yMZhZlu5+9v1yrqCVuql5duEyb3N1c7AP/T0TTTDzFrNbB4wF5hvZnPMbL86bVrMbE6T1+tjZnnfXjYLZnZAmbIEPFyyrNO0H3JC3HmGMFSwWXLnsZ6+h064Bviauz8AYGYTgWuBvfIauPt7ZvaUme3g7i82cjF3X2dml0KpCdh2zOxi4IeEydO7gL2BM9z9l3XaXeTuZ9crq3pu31h5hbs/XudWL6P9Qqi8suprtgB3u/shdV47r/2ZkeJlwCx3n92h7nbADkB/M9uHDZPXgwlzQl1OAVmknBWVYAzg7jPMrMywxUhggZk9TJjvqLT/6xJtp5nZMcBUb2xs8VB3P8vMJgF/Bo4jjOcXBmTgU7RfgQohhTIakIFLC17LCXM6Nczso4S5i1EdAuRgaJfaWfui4ZfcW2Y2xN2XFdXN0Zr93J49PgJ4DPiKmf3K3avnsj5NmPfZEfiXqvIVwHebuHZdCsgi5cw0s/8kTM46cALw+0ovsaA3+KNOXPNMwsKn98xsNaGH5u4+uE67LbL/Hg7c6O6vW8GaYDP7KmHx0vvMbG7VU4MIqalR7t4xdbOsLQmLwvrSfqJ+OZTaQ2UNMM/MptP+l9zpJdqOAPZ195UAZnYuIWHgQEJa6vqA7O5TgClmdoy731LitTtNk3oiJZjZfQVPu7tHe4NZ2x2Bse5+n5n1A1rcfVVe/c4ysx8RVs+uJqx0HQrc4e4fzqk/BBgGXEiYx6hY4e6vl7zmHoTVuOtTOt09tuCrUr8FuMndG97EysxOiZVnAbRe2wXA3u7+TvZ4K2C2u3/AzJ5w930ibYYSUnQrWVD3Az9osodefH8KyCLdx8xOBf4OGOLuu5nZOODyMmOgFrq1JwO7uvv5ZrYTsL27zyzRdhiwPPuKvzUw2N1fKXnP29A+sL5Qp/65wCcIAfl/CcMcM+oFWzP7XdEvsjpt+wM7u3tDcy5m9k/AJEIaKYQtBm4jDL/8l7ufHGlzCyF3vhLwP0cI6mWGnRqigCxSQtaLPJcGe0lmNpvQS3200vsys3nuvmeJa15BWEz0yawHNwyY5u4fqtPuOOAud19hZt8nTJL9sN4km5kdSRgrHU1Y4r4LsMDd8xYPVdrNI0wcPuHue5vZtsDP3P3IOu0uJWyH8CvaDz1MzW204T5/DGzp7rua2QTC38VRRe2q2rcS9o4xwi+Otjr1Z7v7hHplXUFpbyLlXEOYzDk++1lOyLKoZ03l6zGs/6pe1ofd/euEMVPc/Q3C+Gs9/5QF44mEiakphE2o6vkh8BHgWXfflbC5Ve4YcpXV7r4OWJvlaS8mrO6sZzhhpe4nCT3VIwnbE9QzmfBL7k2ALDti1xLtyOq3EeYCpgKLzWznOk1WZ58lsD41r1uW/2tST6Sc3dz9mKrH52W933oeNLOzgH5mdhBhCfwdJa/5bhbAHcDMRhF6zPW8l/33COAKd/+NmU0ucz13X5rlQPfJxrwvKtGuLRtnvYowMbYSqDus4u5/W+K1Y9a6+7IOE5Wlvuqb2VGE4YnKt4CdgachdwsBgK8SJveGEHrVrxNW/HY5BWSRclab2UR3nwEN9ZLOAr5E+Ef/DcIeLWU3zPp3wrYE25jZBYQMhO8XNwHgxSwj5BDgomziqsy34TfNbCDwAHC9mS0m7G5YyN2/lv3xSjO7izBePTevvpmd5e4Xm9llRAJpiWyJ+Wb2WaDFzMYCpwMP1bvPzPmEbwH3uPs+2S/Jwj1ish743lnvH3dfXvJaDdMYskgJZrY3YS/tIVnRG8ApRYEn8hpDgdHu/lQDbcYThg4MuNfdF5RoszXwV8A8d3/OzLYH9nT3aXXaDSAMj1QmE4cA17v70pz6TS0MMbMj3f32ZrMlsvf3PcL2vADTgPPdfU1Ru6xtm7u3Ziso98kW4Mx09/0L2jQ1f9AMBWSREsxsV3d/vrqXVCmr0+5ewqx+CzCH8HV3urv/Y4lr/hshNaxs76+67URCqt212VDHwHr3mrXbFqhMGs5098UFdSupgP0Iiy3mEIL5XoRJzDI7PjbMzMa4+8IOZR9y97orQc3sHkJK4IWERTuLgQ+5e+4y9Y2ZZdEtx5DoRz+b2w/weKRsVol2T2T//SLh67IBc0te8xRCGtkfgUuA1pLtziWsRHs2ezwaeLBEu+MJhwtMIXwbeB44tkS7/yb0wCuP9wCuK9FuHGFv4WnA7yo/Zf4ugB2qHh9I+DZQ5rMZQBi+6Zt9vqcDI+q0mV2mrCt+NIYsUiAbMvggMMTMqntEg4nva91R36yHehxwjrt70aq5ar5hpdhw4BjCePDO7h47OafaJMLRaY9nr/OSmUX3+u7ge4Te4mJYP4l4D2ElW5Hx7j6v6r7nZ6lo9fwKuBL4GRsmIsv4MvDrLP1tX+CfYcPp5kV8w4KcdWb2W2CpZxG2QLPzBw1TQBYp9heEVKyhhLSsihXAaSXaX0AYc5zh7jPN7H3UnuRSz/sJR2iNAcqMP7+TBf5KdsaAktfp4+2HKJZSbjJwgZn9jLBXhgN/QzhwoZ617l4mHa8dd3/MzE4n9KzXAJ9y99eK2pjZRwjL2F8nfFP5BWHIoo+Zfd7d7ypovtGyLDSGLFLAzE4i/MMf5+7dsuViwbUvIhxW8CfgfwibDL1Zot23CAsuPkUYKz0VuMHdL6vT7hLC+O+NWdEJhOGVvM2FKu36EYJWZdLrD4R0u+gkW9bjhzBcsJiQSbJ+b2HPWa5tZrfTPitjd+BlwgQrXrAwxMzaCBsCDSEMkxzm7o9k34Bu9MiS6chrKMtCpCeZ2bcJs/lbAPcSTlCZWeJrbqX9VcRTu75Uou1XgJvdfUlDNx3afopw30bYrnJ6Qd33A9u6+4PZsMzErN0bhCyLPzV6/Tr39jztz2hs9/m4e3RRiZn9ZdHruvv9ec9Vr6wzswXu/oGq56J7WFQ9P4IwLj8xu9cZhCyLaPZJZyggi5SQjcEeQkgn25/wlfwuQrB7taDdCVUP+xHGd/+/u/99yeseRVW6lbvfXqd+w/sFm9kdwHe9QwpftsT4XK+/BPoAwuq5XagaBi0IrPsTPoOXs8enEMbIFwKT83rInWFmj7v7vh3/HHscaTud0OuvbF96MvCJRj7j0vepgCzSODPbnbCJzqHu/ukG2vUhpL3VPcbKzC4kBP/rs6KTgDZ3/06ddrcBn/OSebJmNt/d98h5ru6+G2b2NPBNwiq99ZNzeT1IM3scOMTDtqAHErI0/p5wxuUHvP6mRB8hbGT/AcJS8hZglRdsS2pm7xH2yzCgP6w/rNiAfu6+RUHbWe6+X4eyNndvLbrPZmhST6QEM7u3Ooi6+1NmdlmZwNrBroSeZBlHABM87BOBmU0hnCpeGJBpfL/gomyRjgf+xixz9ztL1KtoqeoFn0DYZe0W4BYrtxz9p8CJhCyNVuDzhDHzXO7eyB4iHd1nZicSxvEhrJjsloN6FZBFCmQTVlsDIy3stlZ9jM/oEu3fYMN4aWWG/tuFjdobmrWBDasE6/ktjQWMx8zsNHe/qrrQzL5I6PXWc182ITiV9pNzebvLtZhZX3dfS1iFWD2eXiomufsfzazF3d8DrjWzhhfP1GPhRJjK392ZhMwMCD3ylYRx5S6lgCxS7MvAGYTgO4sNAXk58B8l2o+s+vO6spOBmQuBJ7IVcUYYS67XO8bdp2Q5xNRLB8ucAdxqZiezIQC3EoYDJpVoX9n4vvorfO4RToQsjvvNbAkhn7dyTuH7Cefb1fOWmW0JzLZwfuDLhAUfXcrdy+RudymNIYvUkU2Ufdfdz2+y/eFsmJj7fZ2c145ttycsZTbCcuTcTeYtrDg5l7AhvhFyiNcCl7n7D0pc6yDCKjuAJ939d2Xvs1HZOPD2hP2dV2Vl4whLvOvt27wL8CrhF8Y3Cd8cLnf3P3bX/W4sCsgiJZjZw+7ezAnQFxA2Q78hKzoReMjdc3dt68SmPd8krFj7kmf7VmQLUa4gbFj/k0bvvywzO4KworH6pJG6vwQ6cb2mTgxJnQKySAlmdh4wlwZPgLZwaOg+2VgnZtaXsC/GXgVtmjq/z8yeIKxaW9KhfBShJ1p38UMzzOxKwjj7QYRl0McScrW/2E3X69SJISnTGLJIOZUToNeaWWWLSi9KtaoymGw1Ge1PWY7y5k9z3iK2iMTdXzOz3LSuLvAxd9/LzOa6+3kWjmYqPIapkyYT0gF/D2G/YjMb043Xa3r3vEYpIIuU0IkJnouBxy1sw2mEw0DPKWpg7Tcxit1LXrB7J6e83nOdVdlo5y0zG03ICil9pFITYieGdBsLh7i2EvY1uZawavOXhKGoLqWALFLAzMa7+9N547pFE1DZJNu9wH2ETAQj7Pj2Yp3LFq2Mc/J7n3ubWWyfBaPcznTNusPC5vsXsyFL42fdeL3OnBjSjGZ3z2uYArJIsTMJebKXRp4rSu0i23HtjmyVV+mv8N7kWXOdXPzQMDP7EGEJ9PnZ44HAPMJxVd02gUhY1fc9Qs7zjYRjsZrKgCmp2d3zGqZJPZFuZGZXAFfVS+XKaRsd2ujO7IVGdHYJ9Kai2d3zmqEeskhJZvYxwp7E1Rvo/DynbmUl2kTgNDP7Exv2UvCizWyqrKr6cz/Cvsxl9hneWDq7BLopWb7yt6j9u8j9ttIZ7v7jbPe85YRx5HOKds/rDPWQRUows18AuwGz2bCBjuftD1HZQczMdos938yWlhZOj76tkc2MupOZzSfstbE222DoS+7+h8pzeRsWdcF15xBOGum4mVGZZd5JUw9ZpJxWYPcGcpANmgu8BbYGolta9pDOLoFuVlMnjTSqw14W1X/vjaQ8NkQBWaSc+cB2hH0TyhhlZmfmPenu/1LvBcxsHhsCQQswCkhi/BjA3S/I0vkqS6Ar99qHMJbcXW43s69R8qSRZmkvC5HE2IZjgwYRJqtm0j4IRFeHmdnLhCXL0WRZdz+vxLWrt+lcC7yajUv3ahZOHIGSJ410wfW+6O5Xdyj7kbs3smtfKeohixT7cZPtXm42GyLb8vMrhMNN5wFXKxC3S7PbNXvc7qSRbrz0sWa2xt2vz657Od2U160eskgJWe7pandfl83yjwfudPd3c+oXntNW51o3Ae8SxmQPAxa5+zeavPXNRk+l2WUbGd0GXEP4+3jd3c/olmspIIvUZ2azgI8Dw4BHgDbgLXc/Oaf+8GbHNK3q2KRsM6KZJdPkNmtmNsfd987+/B/Aa+4+OXu8/hDTLrze8KqHg4BfAw+SLX3v6jFr0JCFSFnm7m9lp2hc5u4XF+XadvIf6/ped5ZS1omX2qx0+qSRBs2ifZaFEY7VOiJ73OVj1grIIuWYmX2UcOJwZVvJ7lqqXL0nhQH9s8fdlm61idioaXaVseqNSQFZpJxvEI5PutXdn8w2fi/at7hpG3tPik1FD6bZNbRKs1PX0RiySGPMbDsvOEpJNi+NrtLs1LUUkEUaU1kW3dP3IRuHmS2gsVWaTevT3RcQ2ZRlWQ41xRv9RqQnVVZpdjuNIYsUmwl07A1f1RM3Ij1mJPCUmZVapdkZCsgixWp6w+5+eU/ciPSYyRvrQhpDFilgZn8GcjcCKrNJkGxezOwA4LPu/vWufm31kEWKtQAD0bhxr2ZmE4DPAscDzwO3dMd1FJBFijW9SZBs2rI9S04ETgKWAjcRRhUO6q5rKiCLFFPPuPd6mrAa8Eh3/yOAmX2zOy+otDeRYgf39A1IjzkGeAW4z8yuMrOD6eZf0JrUExEpkG29ejRh6OKTwBTCEvppXX6sbhcDAAAAN0lEQVQtBWQRkXKyLTmPA07ojlOuFZBFRBKhMWQRkUQoIIuIJEIBWUQkEQrIIiKJUEAWEUnE/wEglj+zSrYAmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization show\n",
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'callback' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-c7a0dea5db9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'callback' is not defined"
     ]
    }
   ],
   "source": [
    "print(callback.losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
